// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2016
// Mehdi Goli    Codeplay Software Ltd.
// Ralph Potter  Codeplay Software Ltd.
// Luke Iwanski  Codeplay Software Ltd.
// Contact: <eigen@codeplay.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#define EIGEN_TEST_NO_LONGDOUBLE
#define EIGEN_TEST_NO_COMPLEX
#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
#define EIGEN_USE_SYCL

#include "main.h"
#include <unsupported/Eigen/CXX11/Tensor>

template<typename DataType, int DataLayout, typename IndexType>
static void
test_sycl_random_uniform(const Eigen::SyclDevice& sycl_device)
{
	Tensor<DataType, 2, DataLayout, IndexType> out(72, 97);
	out.setZero();

	std::size_t out_bytes = out.size() * sizeof(DataType);

	IndexType sizeDim0 = 72;
	IndexType sizeDim1 = 97;

	array<IndexType, 2> tensorRange = { { sizeDim0, sizeDim1 } };

	DataType* d_out = static_cast<DataType*>(sycl_device.allocate(out_bytes));
	TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> gpu_out(d_out, tensorRange);

	gpu_out.device(sycl_device) = gpu_out.random();
	sycl_device.memcpyDeviceToHost(out.data(), d_out, out_bytes);
	for (IndexType i = 1; i < sizeDim0; i++)
		for (IndexType j = 1; j < sizeDim1; j++) {
			VERIFY_IS_NOT_EQUAL(out(i, j), out(i - 1, j));
			VERIFY_IS_NOT_EQUAL(out(i, j), out(i, j - 1));
			VERIFY_IS_NOT_EQUAL(out(i, j), out(i - 1, j - 1));
		}

	// For now we just check thes code doesn't crash.
	// TODO: come up with a valid test of randomness
	sycl_device.deallocate(d_out);
}

template<typename DataType, int DataLayout, typename IndexType>
void
test_sycl_random_normal(const Eigen::SyclDevice& sycl_device)
{
	Tensor<DataType, 2, DataLayout, IndexType> out(72, 97);
	out.setZero();
	std::size_t out_bytes = out.size() * sizeof(DataType);

	IndexType sizeDim0 = 72;
	IndexType sizeDim1 = 97;

	array<IndexType, 2> tensorRange = { { sizeDim0, sizeDim1 } };

	DataType* d_out = static_cast<DataType*>(sycl_device.allocate(out_bytes));
	TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> gpu_out(d_out, tensorRange);
	Eigen::internal::NormalRandomGenerator<DataType> gen(true);
	gpu_out.device(sycl_device) = gpu_out.random(gen);
	sycl_device.memcpyDeviceToHost(out.data(), d_out, out_bytes);
	for (IndexType i = 1; i < sizeDim0; i++)
		for (IndexType j = 1; j < sizeDim1; j++) {
			VERIFY_IS_NOT_EQUAL(out(i, j), out(i - 1, j));
			VERIFY_IS_NOT_EQUAL(out(i, j), out(i, j - 1));
			VERIFY_IS_NOT_EQUAL(out(i, j), out(i - 1, j - 1));
		}

	// For now we just check thes code doesn't crash.
	// TODO: come up with a valid test of randomness
	sycl_device.deallocate(d_out);
}

template<typename DataType, typename dev_Selector>
void
sycl_random_test_per_device(dev_Selector s)
{
	QueueInterface queueInterface(s);
	auto sycl_device = Eigen::SyclDevice(&queueInterface);
	test_sycl_random_uniform<DataType, RowMajor, int64_t>(sycl_device);
	test_sycl_random_uniform<DataType, ColMajor, int64_t>(sycl_device);
	test_sycl_random_normal<DataType, RowMajor, int64_t>(sycl_device);
	test_sycl_random_normal<DataType, ColMajor, int64_t>(sycl_device);
}
EIGEN_DECLARE_TEST(cxx11_tensor_random_sycl)
{
	for (const auto& device : Eigen::get_sycl_supported_devices()) {
		CALL_SUBTEST(sycl_random_test_per_device<float>(device));
#ifdef EIGEN_SYCL_DOUBLE_SUPPORT
		CALL_SUBTEST(sycl_random_test_per_device<double>(device));
#endif
	}
}
